3D Human Pose Estimation Using Spherical Model Constraints
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Solution Overview
Problem
Current 3D human pose estimation methods face challenges with noise and occlusion in depth data, requiring high configuration hardware and precise human body models, limiting real-time and high-precision tracking.
Innovation Solution
A method using a cloud point human body model with visible spherical distribution constraint, combining global translation and local rotation transformations, and a dynamic database for error recovery, enabling fast and accurate pose tracking without high hardware support or precise models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If discriminant method is used for 3D human pose estimation, then adaptability to different body types is improved, but precision in complex motion cases deteriorates
Solution Approach 1:
The method segments the human body into multiple spherical components (head, torso, limbs) that can independently transform. Each body part is represented as a sphere with adjustable parameters, allowing the model to adapt to different body types while maintaining precision through localized transformations of individual segments rather than global rigid transformations.
Solution Approach 2:
The patent implements dynamic spherical distribution constraints that adapt during motion. The spherical model allows for dynamic adjustment of body part positions and orientations through rotation and translation operations, enabling the system to handle complex motions while maintaining adaptability to various body types through real-time parameter updates.
2Measurement precision
If generating method with complex human body model is used, then precision in data loss cases is improved, but susceptibility to local optimization in fast complex motion increases
Solution Approach 1:
The patent changes the parameter representation from complex mesh models to simplified spherical parameters (center coordinates, radii, rotation angles). This parameter simplification reduces the search space and avoids local optimization traps while maintaining sufficient precision through careful parameter optimization and constraint enforcement during the matching process.
Solution Approach 2:
The method uses a simplified spherical copy of the human body model that captures essential geometric and pose information without the complexity of detailed mesh models. This spherical representation serves as an effective approximation that maintains precision for pose estimation while avoiding the computational complexities and local optimization issues of more detailed models.
3Measurement precision
If large number of training data sets and pose database are used, then recognition accuracy is improved, but hardware configuration requirements increase
Solution Approach 1:
The patent employs lightweight spherical model representations instead of heavy mesh models and large pose databases. The spherical parameters require minimal storage and computational resources, enabling high-precision pose estimation on general hardware configurations without requiring GPU acceleration or large-scale training datasets.
Solution Approach 2:
By changing from complex model parameters to simple spherical parameters, the patent dramatically reduces computational requirements. The optimization involves solving for a small number of spherical parameters (positions, radii, rotations) rather than optimizing complex mesh vertex positions, enabling real-time performance on general hardware while maintaining recognition accuracy.
4Measurement precision
If accurate 3D human model is used, then pose estimation precision is improved, but computational complexity and hardware requirements increase
Solution Approach 1:
The patent applies local quality by using spherical approximations for different body parts with locally optimized parameters. Each sphere represents a specific body part with its own center, radius, and orientation parameters, allowing precise local representation of body geometry and pose while keeping overall computational complexity low through the simplicity of spherical mathematics.
Data Source
AI summary
The invention discloses a method for three-dimensional human pose estimation, which can realize the real-time and high-precision 3D human pose estimation without high configuration hardware support and precise human body model. In this method for three-dimensional human pose estimation, including the following steps: (1) establishing a three-dimensional human body model matching the object, which is a cloud point human body model of visible spherical distribution constraint. (2) Matching and optimizing between human body model for human body pose tracking and depth point cloud. (3) Recovering for pose tracking error based on dynamic database retrieval.